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Network Embedding

Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 301–310 of 403 papers

TitleStatusHype
Tag2Vec: Learning Tag Representations in Tag Networks—0
Compositional Network Embedding—0
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
Multimodal Deep Network Embedding with Integrated Structure and Attribute Information—0
Subgraph Networks with Application to Structural Feature Space Expansion—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Splitter: Learning Node Representations that Capture Multiple Social ContextsCode0
Multi-Hot Compact Network Embedding—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
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